.png){kind=icon}
.png){kind=icon}
Return
A Survey on Distributed Graph Pattern Matching in Massive Graphs
.png){kind=icon}
OA
AI
DOI：10.1145/3439724.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.
Keywords:
Graph pattern matching
distributed graphs
graph simulation
subgraph isomorphism
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
28
Papers:
2.4K
Citations:
3.5W
View more organizations